Recognizing Accented Speech

ABSTRACT

Techniques ( 300, 400, 500 ) and apparatuses ( 100, 200, 700 ) for recognizing accented speech are described. In some embodiments, an accent module recognizes accented speech using an accent library based on device data, uses different speech recognition correction levels based on an application field into which recognized words are set to be provided, or updates an accent library based on corrections made to incorrectly recognized speech.

RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.15/464,668, filed on Mar. 21, 2017, which is a continuation of U.S.application Ser. No. 13/772,373, filed on Feb. 21, 2013, the contents ofboth are incorporated herein by reference in their entirety.

BACKGROUND

Current speech-recognition technologies are quite poor at recognizingspeech when spoken with an accent. To address this problem, one partialsolution tracks corrections made by a user in response to a currenttechnology's failure to correctly recognize a word. This partialsolution can be frustrating to users with accents, as they often have tocorrect many incorrectly recognized words before these currenttechnologies improve their recognition, often so many times that a usergives up on voice recognition entirely. Even for those users that takethe time and endure the frustration, many current technologies stillinadequately recognize a user's speech when that user has an accent.

Another partial solution to address this problem requires a user to goto a special user interface and speak a list of particular words.Requiring users with accents to find this special user interface andspeak a list of words does not provide an excellent user experience, andthus often will simply not be performed by users. Further, requiringthis effort from users does not enable current technologies to recognizeaccents sufficiently well. Further still, even if a user that owns adevice goes to this effort, it is unlikely to be performed by anotheruser borrowing the owner's device, such as when a device's owner isdriving and a passenger uses the owner's device.

BRIEF DESCRIPTION OF THE DRAWINGS

Techniques and apparatuses for recognizing accented speech are describedwith reference to the following drawings. The same numbers are usedthroughout the drawings to reference like features and components:

FIG. 1 illustrates an example environment in which techniques forrecognizing accented speech can be implemented.

FIG. 2 illustrates example linguistic and accent libraries of FIG. 1.

FIG. 3 illustrates example methods for recognizing accented speech usingan accent library determined based on device data.

FIG. 4 illustrates example methods for altering an accent library tomore-accurately recognize accented speech.

FIG. 5 illustrates example methods for recognizing speech at a speechrecognition level based on an application field, which may use an accentlibrary.

FIG. 6 illustrates an example application having application fields.

FIG. 7 illustrates various components of an example apparatus that canimplement techniques for recognizing accented speech.

DETAILED DESCRIPTION

Current techniques for recognizing accented speech often are quite poorat recognizing speech when spoken with an accent. This disclosuredescribes techniques and apparatuses for recognizing accented speechusing an accent library, and, in some embodiments, using differentspeech recognition correction levels based on an application field intowhich recognized words are set to be provided.

The following discussion first describes an operating environment,followed by techniques that may be employed in this environment, anexample application having application fields, and proceeds with exampleapparatuses.

FIG. 1 illustrates an example environment 100 in which techniques forrecognizing accented speech can be implemented. Example environment 100includes a computing device 102 having one or more processors 104,computer-readable storage media (storage media) 106, a display 108, andan input mechanism 110.

Computing device 102 is shown as a smart phone having an integratedmicrophone 112 as one example of input mechanism 110. Various types ofcomputing devices and input mechanisms may be used, however, such as apersonal computer having a separate, standalone microphone, a cellularphone connected to a pico-net (e.g., Bluetooth™) headset having amicrophone, or tablet and laptop computers with an integrated stereomicrophone, to name but a few.

Computer-readable storage media 106 includes an accent module 114,device data 116, mined data 118, and applications 120. Accent module 114includes a linguistic library 122 and one or more accent libraries 124.Accent module 114 may operate with, operate without, include, beintegral with, and/or supplement a speech recognition engine (notshown). Accent module 114 is capable of recognizing accented speech,such as by determining, based on device data 116, an accent library ofaccent libraries 124 to use to recognize speech in conjunction withlinguistic library 122.

Linguistic library 122 is associated with a language or dialect thereof,such as Australian English, American (US) English, British (King's)English, and so forth. Linguistic library 122 and a known speechrecognition engine may operate to perform known speech recognition,though use of either or both is not required. Thus, accent module 114,in some embodiments, uses one of accent libraries 124 to supplement aknown speech recognition engine using a known type of linguistic library122.

By way of example, consider FIG. 2, which illustrates example linguisticlibraries 122 and accent libraries 124 of FIG. 1. Here two examplelinguistic libraries, Australian English 204 and US English 206, areshown. Associated with each of these linguistic libraries 204 and 206,are numerous accent libraries 208 and 210, respectively.

Accent libraries 208 include eight examples, though many more arecontemplated by the techniques, including Australian (AU)English-Mandarin 208-1, AU English-New South (N.S.) Wales 208-2, AUEnglish-New Zealand (NZ) Auckland 208-3, AU English-NA Christchurch208-4, AU English-Scuba-Diver 208-5, AU English-Outback 208-6, AUEnglish-Perth 208-7, and AU English-Indonesia 208-8. As is clear fromthe names, each of these accent libraries is associated with a largelanguage group (Australian English) and accents present within thatlanguage group, whether it be recent Mandarin-speaking immigrants orpersons involved in scuba diving.

Similarly, accent libraries 210 include eight examples, USEnglish-Mandarin 210-1, US English-Cantonese 210-2, US English-Boston210-3, US English-Surfer 210-4, US English-Hearing Impaired 210-5, USEnglish-Rural 210-6, US English-South 210-7, and US English-Alaska210-8. Note that the Mandarin accent libraries 208-1 and 210-1 can bedifferent, as each is associated with a different linguistic library.There may, however, be some common elements between the accent librariesdue to common traits of Mandarin speakers, whether speaking English inan Australian dialect or a US dialect. Note that these accent librariesare nearly unlimited in number and in accents addressed. Regionalaccents, accents common to small or large immigrant groups, interestsand subcultures, and even common physical characteristics, such aspersons that are hearing impaired having some commonality in accent.

In the example of FIG. 2, each of accent libraries 124 containsupplemental information or algorithms for use by linguistic library122. Here linguistic library 122 is used for a large language group(e.g., that has more, an average, or a median for a larger number ofpersons), which is supplemented by one or more of accent libraries 124.While this example of FIG. 2 associates accent libraries with linguisticlibraries, accent module 114 may forgo use of a linguistic library or aknown speech recognition engine. Accent module 114 may instead provideits own algorithms and engine without use of other engines or libraries,relying instead on accent library 124 without linguistic library 122 butincluding algorithms or information useful for recognizing speech of alarge number of persons.

Accent module 114 may determine which of accent libraries 124 to use torecognize accented speech based on device data 116 and/or mined data 118(both of FIG. 1). Device data 116 can include device personal data 126,as well as data specific to computing device 102. Data specific tocomputing device 102 can include the date of manufacture or purchase ofcomputing device 102 (e.g., a recently-released mobile phone or tablet)and information about computing device 102, such a manufacturer,hardware capabilities, and so forth.

Device personal data 126 includes data created or determined based on auser's interaction with computing device 102, such as names of contacts,installed applications, receiving country or regions of messages, auser's name, contact information, non-standard keyboards (e.g., for aparticular language other than the language for which computing device102 is set), and contextual application information (e.g., searchterms). Thus, names of contacts may indicate a country of origin of theuser or a non-standard type of keyboard may indicate that a languageother than the language setting for the computing device is the user'snative language. Further, a receiving country or region for messages mayinclude addresses in countries in which the language setting for thecomputing device is not a most-spoken language, e.g., the receivingcountry of Indonesia with a setting of Australian English, such as shownin FIG. 2 with Australian English 204 and AU English-Indonesia 208-8.

In more detail, emails or addresses in a user's contacts may indicate anationality or ethnic origin of the user (e.g., Slavic first or lastnames). The addresses may indicate a native location of, or currentlocation of, the user, as well as other details about the user that maybe used to determine an accent library 124 for the user. Names in emailaddress lines or text in those emails may indicate the user's friends'nationalities, origins, subcultures, or the user's business, or theuser's interests. These interests, as noted further below, may indicatean accent, such as user's interest in surfing, scuba diving, or cooking.Some words and how these words are spoken can depend on these interests,and thus subcultures.

A person involved in scuba diving, for example, may use the terms“re-breather” and “Barotrauma,” which, but for an accent libraryassociated with scuba diving, might be incorrectly recognized.Similarly, a person involved in surfing might use the terms “goofyfoot,” “cutback,” or “closed out,” which might also be incorrectlyrecognized from a user's speech. Finally, for the cooking enthusiast,“La Creuset,” “rotisserie,” and “braising,” may be incorrectlyrecognized without the current techniques.

Device personal data 126 may also include other information useful indetermining an accent and thus an accent library, such asSlavic-language books in the user's e-book library, Slavic-language newsarticles, articles and books about Poland, a saved weather channel forWarsaw, Poland, information about fishing in Estonia, a web search entryfor accordion music, polka music in the user's music library, and soforth.

Mined data 118 may also or instead be used by accent module 114 todetermine which of accent libraries 124 to use to recognize speech.Mined data 118 includes mined personal data 128, which may include anypersonal data that may be found about a user of computing device 102,either through the Internet or otherwise. Thus, mined personal data 128may include the user's search terms, purchases, location, demographics,income, and so forth.

As noted, computer-readable storage media 106 also includes applications120, such as email application 130, social network application 132, orspreadsheet application 134 all of FIG. 1. Each of applications 120includes one or more application fields 136, which, in some embodiments,are used to determine a speech recognition correction level. By way ofexample, consider spreadsheet application 134. Here a number-only cell138 and a general-text cell 140 are each an example of application field136. Number-only cell 138 may require more-precise text thangeneral-text cell 140, and thus a different speech recognitioncorrection level.

FIG. 3 illustrates example methods 300 for recognizing accented speechusing an accent library determined based on device data. The order inwhich blocks of these and other methods are described is not intended tobe construed as a limitation, and any number or combination of thedescribed blocks in these and other methods herein can be combined inany order to implement a method or an alternate method.

At block 302, device data is received for a computing device. Devicedata can be received responsive to an active retrieval performed atblock 302. Thus, using environment 100 of FIG. 1 as an example, accentmodule 114 may retrieve device data 116 at block 302, such as bysearching contact data on computing device 102 and technological detailsabout computing device 102.

As noted in part above, device data 116 may include device personal data126 and other, non-personal data associated with computing device 102.By way of one ongoing example, assume that device data 116 indicatesthat computing device 102 is a smartphone released just 30 days ago thathas significant computing power. This may be used in part to determinean appropriate accent library 124 based on demographics indicating thatusers of this smartphone, at least when recently released, are earlyadopters, technologically savvy, and aged between 18 and 32.

Assume that device personal data 126 includes contact names andaddresses indicating a statistically relevant quantity of Asian lastnames and Asian first names. This statistical relevance can bedetermined in various manners, such as by comparison with a typicalperson's contact list that uses the same linguistic library 122. Thus,while the average number of Asian first names for an American (US)English linguistic library user's contact list may be 1.3% and Asianlast names 11% assume here that this user's contact list has 14% Asianfirst names and 29% Asian last names. Statistical analysis considersthis statistically relevant based on it being one or more standarddeviations from average. This indicates a likelihood that the user maynot be a native English speaker or that family members of the user arelikely not to be a native English speaker, especially thestatistically-relevant quantity of Asian first names, as Asian firstnames are more likely to indicate a first-generation immigrant thanAsian last names.

In addition to this information from a user's contact list, assume thatdevice personal data 126 indicates that the user's name is “Molly Chin,”substantial numbers and durations of trips to the beach, a purchase ofsurfing gear, and that the user lives in southern California.

At block 304, an accent library is determined based on the device datareceived. This accent library is determined for use in speechrecognition. Continuing the ongoing embodiment, assume that accentmodule 114 correlates device data 116 with known accents associated withthis type of device data, thereby determining that two different accentlibraries 124 are likely, that of US English-Mandarin 210-1 and USEnglish-Surfer 210-4 both of FIG. 2. Assume that the surfer accentlibrary is determined to be more likely based on the young age projectedfor the user (as an early adopter and so forth), trips to the beach, anEnglish first name (Molly), surfer-based purchases, and so forth. Inthis ongoing example, accent module 114 determines accent libraries 124based on device data 116, though accent module 114 may also or insteadbase this determination on mined data 118 and information about priorspeech received by computing device 102.

At block 306, speech is received at the computing device. Speech can bereceived in various manners, such as input mechanism 110 describedabove. Continuing the ongoing example, assume that the user says thefollowing for entry into a text message to a friend “Jean, is it closedout?”

At block 308, speech is recognized based on the accent library.Concluding the ongoing example, accent module 114 uses, along with aspeech recognition engine, linguistic library US English 206 and accentlibrary US English-Surfer 210-4 selected based on device data as notedabove. Here assume that, absent the accent library, that a speechrecognition engine would recognize Molly's speech of “Jean, is it closedout?” as “Jean, is it close now?” Due to accent library USEnglish-Surfer 210-4, however, accent module 114 acts to correctlyrecognize Molly's speech as “Jean, is it closed out?” Accent module 114then passes this text to the text field.

This recognition is due, in this example, to accent module 114 beingable to select between multiple options for how to recognize Molly'sspeech, including an option that, but for the accent library, would havebeen considered a low-probability option for a current speechrecognition engine relative to other likely options of “close now,”“hosed out,” and “closet.” Here accent library US English-Surfer 210-4adds words, changes probabilities of words and phrases, and altersalgorithms to change how certain sounds are interpreted (e.g., surfershave a different speech pattern, which is part of an accent, not justthe words used).

Alternatively or additionally, methods 300 proceed to blocks 310 and/orblocks 312-318. At block 310, the accent library is updated based oncorrected errors made during recognition of the speech. Block 310 maywork in conjunction with, or separate from, methods 400 as describedbelow. In the above example methods 300 correctly recognized Molly'sspeech. Were it incorrect, correction by the user (Molly Chin) can berecorded and used to update the accent library.

At block 312, other speech is received at the computing device, theother speech received from a different speaker than the speech receivedat block 302. By way of example, assume that Molly passes her smartphone to her father because she is driving. Assume that Molly asks herfather to request a good Thai restaurant. Assume also that her father isa native Mandarin speaker and that English is a second language for him.Further, assume that, like many native Mandarin speakers, Molly's fatheruses tones to differentiate words, while English speakers use intonation(pitch patterns in sentences). Further, assume that Molly's father, likemany Mandarin speakers, has problems pronouncing “I” sounds at the endof a syllable. Thus, Molly's father pronounces “why” as “wiw,” “fly” as“flew,” and “pie” as “piw.” Thus, when Molly's father asks the smartphone to find a Thai restaurant by saying “Find Thai Restaurant” butthat, due to his accent, it sounds to a native US English speaker (or aspeech recognition engine using only a US English library) as “Find TewRestaurant.”

At block 314, the other speech is dynamically determined not to beassociated with the accent library determined at block 304. Accentmodule 114 determines, in real time on receiving the speech “Find TewRestaurant” that the speaker is not Molly and thus that accent libraryUS English-Surfer 210-4 does not apply. Accent module 114 may determinethis based on the “Tew” or other indicators, such as tonal varianceswithin the word “Restaurant,” which is common to both Mandarin andCantonese speakers, or simply that a history of speech received fromMolly indicates that it is not Molly. This can be performed in numerousways, such as Molly having a generally high-pitched voice and Molly'sfather not having this high pitch, speaking speed differences betweenMolly and Molly's father, and so forth.

At block 316, another accent library, or no accent library, isdetermined for the other speech. Continuing this example, assume thataccent module 114 determines, based on tonal variances within the word“Restaurant” that Molly's father is either a native Mandarin orCantonese speaker. Further, assume that accent module 114, determinesthat Molly's personal data indicates that she has friends and addressesassociated more closely with a region of China in which Mandarin is thedominant language (e.g., Beijing) rather than regions associated withCantonese (e.g., Hong Kong). This information may have already beendetermined at block 304 as noted above.

At block 318, the other speech is recognized with the other accentlibrary or with no accent library, as determined above. Concluding theongoing example, accent module 114 recognizes Molly's father's speech of“Find Tew Restaurant” as “Find Thai Restaurant” by using accent libraryUS English-Mandarin 210-1 of FIG. 2 rather than incorrectly recognizethis speech as “Find Two Restaurants.”

FIG. 4 illustrates example methods 400 for altering an accent library tomore-accurately recognize accented speech.

At block 402, a correction to a speech element is received. Thiscorrection corrects a speech element that was incorrectly recognizedusing an accent library. The correction can be received from a remotecomputing device, though this is not required. As noted in block 310,speech recognition using an accent library may be incorrect and thencorrected by a user. One or many corrections associated with an accentlibrary can be received, such as from thousands of remote computingdevices (e.g., smart phones, laptops, tablets, desktops and so forth).The computing device can be computing device 102 of FIG. 1 but in thisembodiment is a server computer remote from computing device 102 and atwhich corrections are recorded and accent libraries 124 are updated toimprove recognition.

At block 404, an accent library is altered to provide an updated accentlibrary, the updated accent library able to more-accurately recognizethe speech element. Using one of the above examples to illustrate,assume that the accent library US English-Mandarin 210-1 incorrectlyrecognized Molly's father's speech as “Find The Restaurant” instead of“Find Thai Restaurant.” Assume also that Molly's Father corrected theincorrect recognition to “Thai.” This correction, and many others likeit for the same accent library, can be sent to, and received by, anupdating entity. The updating entity can be accent module 114 oncomputing device 102, or another accent module or other entity on aserver computer.

At block 406, the updated accent library is provided to the remotecomputing device or devices effective to enable the remote computingdevice or devices to more-accurately recognize the speech element. Thus,the speech element “Tew” will be more likely to be correctly recognizedas “Thai” than “The” using the updated accent library.

Furthermore, device data can also be received from the remote computingdevice or devices that is associated with a user of the remote computingdevice and based on which the accent library was determined to be usedfor speech recognition of speech from the user. Thus, information aboutMolly for corrections to accent library US English-Surfer 210-4 orMolly's father for accent library US English-Mandarin 210-1 can beprovided.

The update to the appropriate accent library may then be tailored tocertain device data or other data. This, in effect, may act to providesub-categories of accent libraries over time. Thus, a speaker, such as aperson having similarities to Molly Chin may receive an update for USEnglish-Surfer 210-4 based on her similarities in age (18-30) and region(Southern California) that another speaker using US English-Surfer 210-4will not, such as a man (aged 45-60) living in a different region(Miami, Fla.). In so doing, updates can be provided to users based onwhether the users or their computing devices have one or more sameelements of device or mined data as the device or mined data of theremote computing device from which the correction was received.

FIG. 5 illustrates example methods 500 for recognizing speech at aspeech recognition level based on an application field, which may use anaccent library.

At block 502, speech is received at a computing device. This can be asset forth in the various examples above.

At block 504, a speech recognition correction level is determined basedon an application field to which recognized text are set to be provided.One example of this can be example application fields 136 of FIG. 1,namely number-only cell 138 and a general-text cell 140 of spreadsheetapplication 134. As noted above, accent module 114 may determine aspeech recognition correction level based on the application fields,such as it likely needing highly accurate speech recognition or lessaccurate and/or faster recognition.

Consider, by way of example, FIG. 6, which illustrates an example emailapplication's user interface 602 having application fields 604 and 606.Application field 604 is an address field and application field 606 is abody field. Assume for example that Molly Chin from the above examplessays “Surf Girl Seven Seven Seven At Gee Mail Dot Com.”

When opening a new email to send to a friend, assume that an emailapplication will receive recognized text first into the email addressfield shown at application field 604. When speaking, and after the emailaddress is complete, assume the email application will receiverecognized text into the body of the email, at application field 606. Inthis example, accent module 114 determines that a maximum level ofcorrection should be used for the address field. In such a case, accentmodule 114 uses an appropriate accent library 124 or makes otherrefinements that improve accuracy. Improving accuracy, however, can comeat a cost in terms of time to recognize text and computing resources(processor and battery), to name but a few. Therefore, higher speechcorrection levels may not always be appropriate.

Note also that accent module 114 may apply different correction levelsby determining to use none, one, or multiple accent libraries 114, suchas both a Mandarin and a Surfer accent library, for example. Further,accent module 114 may determine correction levels without use, or lackof use, of accent libraries 124. For example, accent module 114 may usea different linguistic library 122 for some application fields or use anaccent library 124 that is directed to spoken numbers rather thanaccents in normal speech. Thus, one of linguistic libraries 122 may bedirected to recognizing speech that is numerical or for addresses andanother that is directed to recognizing speech that is conversational.In these and other ways set forth herein, the techniques may act toimprove speech recognition.

At block 506, the speech received is recognized at the speechrecognition correction level to produce recognized text. Thus, forapplication field 604 (the email address field), accent module 114recognizes speech at the determined speech recognition level, here at amaximum level using one or more accent libraries 124 and/or alternativelinguistic libraries 122 directed to the expected speech.

At block 508, recognized words and other text are provided to theapplication field. Concluding the ongoing example for Molly Chin, atblock 508 accent module 114 recognizes the speech of “Surf Girl SevenSeven Seven At Gee Mail Dot Com” not as words but, based on the accentlibrary 124 and/or linguistic library 122, as a combination of words andtext, and also because it is an address field for an email, the “at” asthe “@” symbol. Thus, the speech is recognized as“surfgirl777@GMail.com”.

While not required, the techniques, in some embodiments, use aless-than-maximum speech correction level when the application field isa body of an email, blog, social networking entry, or word-processingdocument. Conversely, the techniques, for address fields, number-onlyfields in spreadsheets, phone numbers, and so forth may use maximumspeech correction levels and/or alternative linguistic libraries 122 oraccent libraries 124.

FIG. 7 illustrates various components of an example device 700 includingaccent module 114 including or having access to other modules, thesecomponents implemented in hardware, firmware, and/or software and asdescribed with reference to any of the previous FIGS. 1-6.

Example device 700 can be implemented in a fixed or mobile device beingone or a combination of a media device, computing device (e.g.,computing device 102 of FIG. 1), television set-top box, videoprocessing and/or rendering device, appliance device (e.g., aclosed-and-sealed computing resource, such as some digital videorecorders or global-positioning-satellite devices), gaming device,electronic device, vehicle, and/or workstation.

Example device 700 can be integrated with electronic circuitry, amicroprocessor, memory, input-output (I/O) logic control, communicationinterfaces and components, other hardware, firmware, and/or softwareneeded to run an entire device. Example device 700 can also include anintegrated data bus (not shown) that couples the various components ofthe computing device for data communication between the components.

Example device 700 includes various components such as an input-output(I/O) logic control 702 (e.g., to include electronic circuitry) andmicroprocessor(s) 704 (e.g., microcontroller or digital signalprocessor). Example device 700 also includes a memory 706, which can beany type of random access memory (RAM), a low-latency nonvolatile memory(e.g., flash memory), read only memory (ROM), and/or other suitableelectronic data storage. Memory 706 includes or has access to accentmodule 114, linguistic libraries 122, and accent libraries 124 and, insome embodiments, a speech recognition engine (not shown).

Example device 700 can also include various firmware and/or software,such as an operating system 708, which, along with other components, canbe computer-executable instructions maintained by memory 706 andexecuted by microprocessor 704. Example device 700 can also includeother various communication interfaces and components, wireless LAN(WLAN) or wireless PAN (WPAN) components, other hardware, firmware,and/or software.

Other examples capabilities and functions of these modules are describedwith reference to elements shown in FIGS. 1 and 2. These modules, eitherindependently or in combination with other modules or entities, can beimplemented as computer-executable instructions maintained by memory 706and executed by microprocessor 704 to implement various embodimentsand/or features described herein. Alternatively or additionally, any orall of these components can be implemented as hardware, firmware, fixedlogic circuitry, or any combination thereof that is implemented inconnection with the I/O logic control 702 and/or other signal processingand control circuits of example device 700. Furthermore, some of thesecomponents may act separate from device 700, such as when remote (e.g.,cloud-based) libraries perform services for accent module 114.

Although the invention has been described in language specific tostructural features and/or methodological acts, it is to be understoodthat the invention defined in the appended claims is not necessarilylimited to the specific features or acts described. Rather, the specificfeatures and acts are disclosed as example forms of implementing theclaimed invention.

1. (canceled)
 2. A computer-implemented method comprising: receiving,from a user, an utterance that was spoken while focus is set on a fieldof a form; selecting a quantity of accent libraries for an automatedspeech recognizer to use in transcribing the utterance based at least ona field type associated with the field; obtaining, from the speechrecognition system, a transcription of the utterance by the automatedspeech recognizer using the selected quantity of accent libraries; andproviding the transcription of the utterance in the field of the form.3. The method of claim 2, wherein the quantity of accent libraries isselected based on demographic data of the user.
 4. The method of claim3, wherein the demographic data of the user includes an age range,gender, native language, and a geographic location where the user islocated.
 5. The method of claim 2, the demographic data of the user isbased on countries of addresses stored in an address book of a computingdevice that receives the utterance.
 6. The method of claim 2,comprising: receiving, from the user, an additional utterance that wasspoken while focus is set on a different field of the form; selecting adifferent quantity of accent libraries for the automated speechrecognizer to use in transcribing the additional utterance based atleast on a field type associated with the different field; obtaining,from the speech recognition system, an additional transcription of theadditional utterance by the automated speech recognizer using thedifferent quantity of accent libraries; and providing the additionaltranscription of the additional utterance in the different field of theform.
 7. The method of claim 2, wherein: the form is an email form, thefield is an email body field, a to field, a cc field, or a subjectfield, and the field type is a general text field or an address field.8. The method of claim 2, wherein the selected quantity of accentlibraries affects an accuracy level of the speech recognition system. 9.A system comprising: one or more computers and one or more storagedevices storing instructions that are operable, when executed by the oneor more computers, to cause the one or more computers to performoperations comprising: receiving, from a user, an utterance that wasspoken while focus is set on a field of a form; selecting a quantity ofaccent libraries for an automated speech recognizer to use intranscribing the utterance based at least on a field type associatedwith the field; obtaining, from the speech recognition system, atranscription of the utterance by the automated speech recognizer usingthe selected quantity of accent libraries; and providing thetranscription of the utterance in the field of the form.
 10. The systemof claim 9, wherein the quantity of accent libraries is selected basedon demographic data of the user.
 11. The system of claim 10, wherein thedemographic data of the user includes an age range, gender, nativelanguage, and a geographic location where the user is located.
 12. Thesystem of claim 9, the demographic data of the user is based oncountries of addresses stored in an address book of a computing devicethat receives the utterance.
 13. The system of claim 9, wherein theoperations further comprise: receiving, from the user, an additionalutterance that was spoken while focus is set on a different field of theform; selecting a different quantity of accent libraries for theautomated speech recognizer to use in transcribing the additionalutterance based at least on a field type associated with the differentfield; obtaining, from the speech recognition system, an additionaltranscription of the additional utterance by the automated speechrecognizer using the different quantity of accent libraries; andproviding the additional transcription of the additional utterance inthe different field of the form.
 14. The system of claim 9, wherein: theform is an email form, the field is an email body field, a to field, acc field, or a subject field, and the field type is a general text fieldor an address field.
 15. The system of claim 9, wherein the selectedquantity of accent libraries affects an accuracy level of the speechrecognition system.
 16. A non-transitory computer-readable mediumstoring software comprising instructions executable by one or morecomputers which, upon such execution, cause the one or more computers toperform operations comprising: receiving, from a user, an utterance thatwas spoken while focus is set on a field of a form; selecting a quantityof accent libraries for an automated speech recognizer to use intranscribing the utterance based at least on a field type associatedwith the field; obtaining, from the speech recognition system, atranscription of the utterance by the automated speech recognizer usingthe selected quantity of accent libraries; and providing thetranscription of the utterance in the field of the form.
 17. The mediumof claim 16, wherein the quantity of accent libraries is selected basedon demographic data of the user.
 18. The medium of claim 16, thedemographic data of the user is based on countries of addresses storedin an address book of a computing device that receives the utterance.19. The medium of claim 16, wherein the operations further comprise:receiving, from the user, an additional utterance that was spoken whilefocus is set on a different field of the form; selecting a differentquantity of accent libraries for the automated speech recognizer to usein transcribing the additional utterance based at least on a field typeassociated with the different field; obtaining, from the speechrecognition system, an additional transcription of the additionalutterance by the automated speech recognizer using the differentquantity of accent libraries; and providing the additional transcriptionof the additional utterance in the different field of the form.
 20. Themedium of claim 16, wherein: the form is an email form, the field is anemail body field, a to field, a cc field, or a subject field, and thefield type is a general text field or an address field.
 21. The mediumof claim 16, wherein the selected quantity of accent libraries affectsan accuracy level of the speech recognition system.